CFD+CD14+ Monocyte Subset Activity Scores from Multi-Omics Analysis of Myasthenia Gravis

A 2026 dataset from figshare by Shuang Li presents results from a multi-omics integration and machine learning analysis of myasthenia gravis. The data likely contains activity scores or signature gene expression for CFD+CD14+ monocyte subsets identified as a potential pathogenic driver. It is derived from integrating scRNA-seq, GWAS, and GTEx data, validated with a new scRNA-seq dataset from MG patients and controls.

Use Cases

• Validate monocyte subset signatures in myasthenia gravis based on the six identified genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, CFD).
• Benchmark machine learning algorithms for feature selection in single-cell omics based on the seven algorithms mentioned.
• Study cell-type-specific TWAS activity in autoimmune diseases based on the described integration method.
• Investigate intercellular communication patterns in immune cells based on the described enhanced activity of CFD+CD14+ monocytes.

Strengths

• Integrates data from multiple authoritative sources (GEO, GWAS catalog, GTEx).
• Identifies six specific signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, CFD) through machine learning.
• Includes a validation dataset from newly generated scRNA-seq on MG patient blood.

Limitations

• The dataset is very small (766.0 B), indicating limited scope or summary-level data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Integration of public multi-omics data (GEO, GWAS, GTEx) and generation of new scRNA-seq validation data, analyzed with TWAS and machine learning.

Freshness

Last updated 2026-05-01 05:25:24.